Biomarkers discovery of diabetic retinopathy through serum metabolomics - Retinal image association analysis with machine learning.

The aim of this study is to develop an innovative method of machine learning combining metabolomic and radiomic analyses for identifying biomarkers to distinguish diabetic retinopathy (DR) patients, non-retinopathy diabetic (NDR) patients and healthy individuals. The serum metabolic profiling of 94 DR patients, 95 NDR patients, and 95 healthy individuals was acquired through the Shimadzu LC-40D X3 and AB Sciex zenoTOF 7600 tandem system. We first conducted a differential analysis on metabolomic data, identifying distinct metabolites and metabolic pathways. Then, we employed Artificial Intelligence (AI) model for learning phenomics (retinal fundus image) and identifying the candidate metabolic biomarkers. Resnet50 was used as the backbone network for DR test. Finally, we performed a correlation analysis between image data and metabolome data. We unveiled the serum metabolic profiling of 94 DR patients, 95 NDR patients and 95 healthy individuals. DR test shows that the AUC (Area Under the Curve) of 0.89 on the independent test set of DR and NDR retinal images. The result of correlation analysis demonstrated 20 significantly metabolites, with three potential biomakers validated through evidenced-based filtering. Using machine learning algorithms, we achieved remarkable accuracy (AUC = 0.99) in distinguishing non-retinopathy diabetic individuals from DR patients using L-carnitine, beta-hydroxymyristic acid, and 5-Methyl-H4SPT. This study synergizes metabolomic and radiomic methodologies to identify biomarkers that can effectively distinguish NDR patients from DR patients. Furthermore, we have proven the feasibility of DR diagnosis through a doctor-free AI model using phenomics-metabolomics analysis. Additionally, exploring the metabolome data may provide new insights into the mechanism of DR.